Proposal for an INRIA PhD fellowship (Cordi-S)

Title of the proposal:

Nonlinear speech analysis for differential diagnosis between

Parkinson's disease and Multiple-System Atrophy

Project Team INRIA: GeoStat (http://geostat.bordeaux.inria.fr/)

Supervisor: Khalid Daoudi (khalid.daoudi@inria.fr)

Scientific context:

Parkinson's disease (PD) is the most common neurodegenerative disorder after Alzheimer's disease.

Prevalence is 1.5% of the population over age 65 and affects about 143,000 French. Given the aging of

the population, the prevalence is likely to increase over the next decade.

Multiple-System Atrophy (MSA) is a rare and sporadic neurodegenerative adult disorder, of

progressive evolution and of unknown etiology. The MSA has a prevalence of 2 to 5/100 000 and has

no effective treatment. It usually starts in the 6th decade and there is a slight male predominance. It

takes 3 years on average from the first signs of the disease for a patient to require a walking aid, 4-6

years to be in a wheelchair and about 8 years to be bedridden.

The PD and MSA require different treatment and support. However, the differential diagnosis between

PD and MSA is a very difficult task because, at the early stage of the diseases, patients look alike as

long as signs, such as dysautonomia, are not more clearly installed for MSA patients. There is currently

no valid clinical nor biological marker for clear distinction between the two diseases at an early stage.

Goal:

Voice and speech disorders in Parkinson's disease is a clinical marker that coincides with a motor

disability and the onset of cognitive impairment. Terminology commonly used to describe these

disorders is dysarthria [1,2].

Like PD patients, depending on areas of the brain that are damaged, people with AMS may also have

speech disorders: difficulties of articulation, staccato rhythm, squeaky or muted voice. Dysarthria in

AMS is more severe and early in the sense that it requires more early rehabilitation compared to PD.

Since dysarthria is an early symptom of both diseases and of different origin, the purpose of this thesis

is to use dysarthria, through digital processing of voice recordings of patients as a mean for objective

discrimination between PD and MSA. The ultimate goal is to develop a numerical dysarthria measure,

based on the analysis of the speech signal of the patients, which allows objective discrimination

between PD and MSA and would thus complement the tools currently available to neurologists in the

differential diagnosis of the two diseases.

Project:

Pathological voices, such as in PD and MSA, generally present high non-linearity and turbulence [3].

Nonlinear/turbulent phenomena are not naturally suited to linear signal processing. The latter is

however ruling over current speech technology. Thus, from the methodological point of view, the goal

of this thesis is to investigate the framework of nonlinear and turbulent signals and systems, which is

better suited to analyzing the range of nonlinear and turbulent phenomena observed in pathological

voices in general, and in PD and MSA voices in particular. We will adopt an approach based on novel

nonlinear speech analysis algorithms recently developed in the GeoStat team [4] and which led, in

particular, to new and promising techniques for pathological voice analysis. The goal will be to extract

relevant speech features to design new dysarthria measures that enable accurate discrimination between

PD and MSA voices. This will also require investigation of machine learning theory in order to develop

robust classifiers (to discriminate between PD and MSA voices) and to make correspondence

(regression) between speech measures and standard clinical rates.

The PhD candidate will actively participate, in coordination with neurologists from the Parkinson's

Center of Pellegrin Hospital in Bordeaux, in the set up of the experimental protocol and data collection.

The latter will consist in recording patient's voices using a numerical recorder and the DIANA/EVA2

workstations (http://www.sqlab.fr/).

References:

[1] Pinto, S et al. Treatments for dysarthria in Parkinson's disease. The Lancet Neurology. Vol 3, Issue 9, 2004.

[2] Auzou, P.; Rolland, V.; Pinto, S., Ozsancak C. (eds.). Les dysarthries. Editions Solal. 2007.

[3] Tsanas A et al. Novel speech signal processing algorithms for high-accuracy classification of Parkinson’s

disease . IEEE Transactions on Biomedical Engineering, 2012; 59 (5):1264-1271.

[4] PhD thesis of Vahid Khanagha. GeoStat team, INRIA Bordeaux-Sud Ouest. January 2013.

http://geostat.bordeaux.inria.fr/images/vahid%20khanagha%204737.pdf

Duration: 3 years (starting fall 2014)

Net Salary: ~1700 € / month (including health care insurance)

Prerequisites: Good level in signal/speech processing is necessary, as well as Matlab and C/C++

programing. Knowledge in machine learning would be a strong advantage.

Candidates should send a CV to khalid.daoudi@inria.fr and also apply via the Inria website:

http://www.inria.fr/en/institute/recruitment/offers/phd/campaign-2014/(view)/details.html?

id=PNGFK026203F3VBQB6G68LOE1&LOV5=4509&LG=EN&Resultsperpage=20&nPostingID=83

24&nPostingTargetID=14059&option=52&sort=DESC&nDepartmentID=28